% Created by Manuel J. Marin-Jimenez and Daniel Rodriguez-Molina

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\begin{document}

\title{LibPaBOD: quick start}
\author{Daniel Rodriguez-Molina and Manuel J. Marin-Jimenez}

\maketitle

\begin{abstract}
This document presents a guide for using LibPaBOD for detecting objects on still images.
\end{abstract}

\section{Introduction}
LibPaBOD is a library written in C++ that implements the popular object detection framework proposed by Felzenszwalb et al.~\cite{felzenszwalb2010}.

\section{Easy detection with LibPaBOD}

Object detection was never so simple! Follow the next steps and you will get the objects detected and drawn on your input image:

\begin{verbatim}
	CvMat *detections = NULL;

	IplImage * img = cvLoadImage ("my_image.jpg", CV_LOAD_IMAGE_COLOR);

	Pabod detector("data/models/person_v6.mat");
	
	detector.detect(img, POSITIVE_INF, &detections);
	
	detector.drawDetections(img, detections);

	cvSaveImage("my_image_dets.jpg", img);
\end{verbatim} 

\noindent So easy!

\section{Using the OpenCV C++ interface}

\begin{verbatim}
	LDetections detections;

	cv::Mat img = cv::imread("my_image.jpg", CV_LOAD_IMAGE_COLOR);

	Pabod detector("data/models/person_v6.mat");
	
	detector.detect(img, POSITIVE_INF, 0.5, detections);
	
	detector.drawDetections(img, detections);

	cv::imwrite("my_image_dets.jpg", img);
\end{verbatim} 

\begin{thebibliography}{}
	 \bibitem{felzenszwalb2010}
	 P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. ``Object Detection with Discriminatively Trained Part Based Models''. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, September 2010.
\end{thebibliography}
}

\end{document}

